Скачать с ютуб 81. Handling Imbalanced Datasets: Techniques and Implementation Guide ⚖️🔧 в хорошем качестве

81. Handling Imbalanced Datasets: Techniques and Implementation Guide ⚖️🔧

Learn how to tackle the challenge of imbalanced datasets in machine learning with this comprehensive guide. 🌟 Discover effective techniques for balancing your data, including Oversampling, Undersampling, SMOTE (Synthetic Minority Over-sampling Technique), and ADASYN (Adaptive Synthetic Sampling Approach). 💡 Follow detailed implementation steps using Python to apply these methods, improve model performance, and achieve more accurate predictions. 📊 This video is ideal for data scientists and practitioners dealing with skewed datasets and looking to enhance model training and evaluation. Let’s dive into practical solutions for fixing imbalanced datasets and elevate your machine learning projects! 🚀✨ 🔗 Link to Repo: https://github.com/codehax41/Machine-... ------------------------------------------------------------------------------------------------------------- 🎬 All Playlist in my channel: 🤖 Machine Learning Playlist:    • Machine Learning Beginner to Expert || End...   ⚙️ ML Ops Playlist:    • MLOps Tutorial with Project Step by Step   🧠 Deep Learning Playlist:    • Плейлист   🎓 Reinforcement Learning Playlist:    • Reinforcement Learning using Python   📈 Stats & Probability Playlist:    • Statistics & Probability for Data Scince   --------------------------------------------------------------------------------------------------------------- 🌐 Connect with me here: 👨‍💻 Github: https://github.com/codehax41 📘 Facebook:   / ramsundar.12380   📷 Instagram:   / mee_iamram   --------------------------------------------------------------------------------------------------------------- 🙏 THANKS & Love you all!!! ❤️ --------------------------------------------------------------------------------------------------------------- #MachineLearning, #AI, #DataScience, #LLM, #GenAI, #ChatGPT #Gemini 🚀 ---------------------------------------------------------------------------------------------------------------